Bearing Fault Diagnosis Method Based on Self-attentional Time-frequency Feature Fusion

Aiming at the problem that the single time and frequency domain signal contains incomplete features in bearing fault diagnosis , which leads to the poor generalization ability of traditional fault diagnosis models, a bearing fault diagnosis method based on self-attention time-frequency feature fusion was proposed. Firstly, the original signal and the corresponding spectrum were taken as dual channel inputs, and the time-frequency cross-attention was able to discover the implicit features in the frequency domain through the time domain, and the implicit features in the time domain were found through the frequency domain. After multi-layer time-frequency cross-attention for global feature extraction, deep fusion time-frequency fusion features were generated. Secondly, the time-frequency fusion features were input into the improved multi-scale residual network to further extract local features, and the dimension was reduced through the fully connected layer and the diagnosis results were output. Finally, the bearing fault data set of Case Western Reserve University and the reducer bearing fault data set of a factory were used for experimental verification. The results show that the diagnostic accuracy and generalization ability of the proposed method are significantly improved, and the diagnostic accuracy of the same working condition can reach more than 99% , and the cross-condition diagnosis accuracy can reach more than 95%.